Identifying Variables Influencing Traditional Food Solid-State Fermentation by Statistical Modeling

Area of Science:

• Biotechnology
• Food Science
• Chemical Engineering

Background:

• Traditional solid-state fermentation (SSF) processes often lack scientific basis, leading to unpredictable outcomes in food production.
• Mathematical models struggle to capture the dynamic complexity of SSF, hindering identification of critical factors affecting yield and quality.

Purpose of the Study:

• To develop statistical models correlating industrial data with final Chinese liquor yield in SSF.
• To identify key variables influencing yield fluctuations in traditional Chinese liquor production.

Main Methods:

• Established statistical models using industrial data: initial starch, water, acid content, starting temperature, and substrate temperature profiles.
• Developed a lactic-acid-inhibition model to explain seasonal yield variations.

Main Results:

• Models based solely on initial conditions and temperature profiles provided unsatisfactory yield predictions.
• Seasonal yield differences, particularly the drop from April to December, were not explained by ambient temperature.
• A lactic-acid-inhibition model suggested lactic acid produced by bacteria as a probable cause for reduced yield.

Conclusions:

• Lactic acid accumulation is a likely factor limiting Chinese liquor yield in SSF.
• Integrated microbiological and engineering studies are essential to pinpoint critical variables for SSF optimization.
• Findings offer insights for improving complex solid-state fermentation processes.